DMBS Architecture May 15 th, 2002

Generic Architecture Query compiler/optimizer Execution engine Index/record mgr. Buffer manager Storage manager storage User/ Application Query update Query execution plan Record, index requests Page commands Read/write pages Transaction manager: Concurrency control Logging/recovery Transaction commands

Query Optimization Purchase Person Buyer=name City=‘seattle’ phone>’ ’ buyer (Simple Nested Loops) Imperative query execution plan: SELECT Q.sname FROM Purchase P, Person Q WHERE P.buyer=Q.name AND Q.city=‘seattle’ AND Q.phone > ‘ ’ SELECT Q.sname FROM Purchase P, Person Q WHERE P.buyer=Q.name AND Q.city=‘seattle’ AND Q.phone > ‘ ’ Declarative SQL query Plan: Tree of R.A. ops, with choice of alg for each op. Ideally: Want to find best plan. Practically: Avoid worst plans! Goal: 

Alternate Plans Find names of people who bought telephony products Purchase Person Buyer=name Category=“telephony” buyer (hash join) Product prod=pname (hash join) Purchase Product Buyer=name Category=“telephony” buyer (hash join) Person prod=pname (hash join)   But what if we’re only looking for Bob’s purchases?

ACID ACID Properties A Atomicity: all actions of a transaction happen, or none happen. C Consistency: if a transaction is consistent, and the database starts from a consistent state, then it will end in a consistent state. I Isolation: the execution of one transaction is isolated from other transactions. D Durability: if a transaction commits, its effects persist in the database.

Problems with Transaction Processing Airline reservation system: Step 1: check if a seat is empty. Step 2: reserve the seat. Bad scenario: (but very common) Customer 1 - finds a seat empty Customer 2 - finds the same seat empty Customer 1 - reserves the seat. Customer 2 - reserves the seat. Customer 1 will not be happy; spends night in airport hotel.

The Memory Hierarchy Main Memory Disk Tape Volatile limited address spaces expensive average access time: nanoseconds 5-10 MB/S transmission rates Gigs of storage average time to access a block: msecs. Need to consider seek, rotation, transfer times. Keep records “close” to each other. 1.5 MB/S transfer rate 280 GB typical capacity Only sequential access Not for operational data Cache: access time 10 nano’s

Main Memory Fastest, most expensive Today: 512MB are common on PCs Many databases could fit in memory –New industry trend: Main Memory Database –E.g TimesTen Main issue is volatility

Secondary Storage Disks Slower, cheaper than main memory Persistent !!! Used with a main memory buffer

Buffer Management in a DBMS Data must be in RAM for DBMS to operate on it! Table of pairs is maintained. LRU is not always good. DB MAIN MEMORY DISK disk page free frame Page Requests from Higher Levels BUFFER POOL choice of frame dictated by replacement policy

Buffer Manager Manages buffer pool: the pool provides space for a limited number of pages from disk. Needs to decide on page replacement policy. Enables the higher levels of the DBMS to assume that the needed data is in main memory. Why not use the Operating System for the task?? - DBMS may be able to anticipate access patterns - Hence, may also be able to perform prefetching - DBMS needs the ability to force pages to disk.

Tertiary Storage Tapes or optical disks Extremely slow: used for long term archiving only

The Mechanics of Disk Mechanical characteristics: Rotation speed (5400RPM) Number of platters (1-30) Number of tracks (<=10000) Number of bytes/track(10 5 ) Platters Spindle Disk head Arm movement Arm assembly Tracks Sector Cylinder

Disk Access Characteristics Disk latency = time between when command is issued and when data is in memory Disk latency = seek time + rotational latency –Seek time = time for the head to reach cylinder 10ms – 40ms –Rotational latency = time for the sector to rotate Rotation time = 10ms Average latency = 10ms/2 Transfer time = typically 10MB/s Disks read/write one block at a time (typically 4kB)

The I/O Model of Computation In main memory algorithms we care about CPU time In databases time is dominated by I/O cost Assumption: cost is given only by I/O Consequence: need to redesign certain algorithms Will illustrate here with sorting

Sorting Illustrates the difference in algorithm design when your data is not in main memory: –Problem: sort 1Gb of data with 1Mb of RAM. Arises in many places in database systems: –Data requested in sorted order (ORDER BY) –Needed for grouping operations –First step in sort-merge join algorithm –Duplicate removal –Bulk loading of B+-tree indexes.

2-Way Merge-sort: Requires 3 Buffers Pass 1: Read a page, sort it, write it. –only one buffer page is used Pass 2, 3, …, etc.: – three buffer pages used. Main memory buffers INPUT 1 INPUT 2 OUTPUT Disk

Two-Way External Merge Sort Each pass we read + write each page in file. N pages in the file => the number of passes So total cost is: Improvement: start with larger runs Sort 1GB with 1MB memory in 10 passes Input file 1-page runs 2-page runs 4-page runs 8-page runs PASS 0 PASS 1 PASS 2 PASS 3 9 3,4 6,2 9,48,75,63,1 2 3,4 5,62,64,97,8 1,32 2,3 4,6 4,7 8,9 1,3 5,62 2,3 4,4 6,7 8,9 1,2 3,5 6 1,2 2,3 3,4 4,5 6,6 7,8

Can We Do Better ? We have more main memory Should use it to improve performance

Cost Model for Our Analysis B: Block size M: Size of main memory N: Number of records in the file R: Size of one record

External Merge-Sort Phase one: load M bytes in memory, sort –Result: runs of length M/R records M bytes of main memory Disk... M/R records

Phase Two Merge M/B – 1 runs into a new run Result: runs have now M/R (M/B – 1) records M bytes of main memory Disk... Input M/B Input 1 Input 2.. Output

Phase Three Merge M/B – 1 runs into a new run Result: runs have now M/R (M/B – 1) 2 records M bytes of main memory Disk... Input M/B Input 1 Input 2.. Output

Cost of External Merge Sort Number of passes: Think differently –Given B = 4KB, M = 64MB, R = 0.1KB –Pass 1: runs of length M/R = Have now sorted runs of records –Pass 2: runs increase by a factor of M/B – 1 = Have now sorted runs of 10,240,000,000 = records –Pass 3: runs increase by a factor of M/B – 1 = Have now sorted runs of records Nobody has so much data ! Can sort everything in 2 or 3 passes !

B: number of frames in the buffer pool; N: number of pages in relation. Number of Passes of External Sort

Next on Agenda File organization (brief) Indexing Query execution Query optimization